Data Mining techniques are being used with increasing frequency by retailers and manufacturers in an attempt to better understand the nature of their business and customers. That is, in most data mining applications, a large amount of transactional data is analyzed without actually having any idea of what the items in the transactions mean or what they say about the customers who purchased those items. For example, in a retail apparel transaction, a basket (i.e., the items purchased by a given customer at one time) may contain a shirt, a tie and a jacket. When data mining algorithms such as association rules, decision trees, neural networks etc. are applied to this basket, they are unable to comprehend the attributes of these products and what those attributes imply. In fact, these items could just as easily be replaced by distinct symbols, such as A, B and C, and the algorithms would produce the same results.
However, in many domains, attribute information is implicitly available and can be used to improve data mining systems by adding an attribute dimension to the data being analyzed. For example, using current data mining techniques, a clothing retailer would know the price, size, and color of a particular shirt that was purchased. However, that same retailer will not be able tell, using these same techniques, what the shirt says about the customer, e.g., this customer tends toward conservative, classic, business wear rather than flashy, trendy, casual wear. These “softer” attributes that say so much about products and the people who buy them tend not to be available for analysis in a systematic way. In a similar vein, such attributes may not only tell something about the people that buy them, but also about the retailers that sell them and/or the manufacturers that provide them. Indeed, a greater understanding of the attributes of product lines being offered by competitors could be used to better develop strategies and aid in planning Thus, a need exists for a technique whereby such attributes may be more readily obtained and applied. With an understanding of such attributes, a variety of services, including recommendation systems and competitive analysis, may be enabled with greater efficiency and efficacy than currently available.